Kyrylo Kolodiazhnyi
  • Male
  • Kharkiv
  • Ukraine
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Profile Information

Job Title:
Senior Software Engineer
Job Function:
Machine Learning, Deep Learning
Software development
LinkedIn Profile:
Contributing, Networking

Kyrylo Kolodiazhnyi's Blog

New book "Hands-on Machine Learning with C++".

Posted on August 25, 2020 at 4:30am 0 Comments

Hi, I'm introducing a book I wrote, "Hands-on Machine Learning with C++." It can help you to discover new ways of using C++ for Machine learning development. It will show you how to use C++ libraries for different aspects of ML from fundamentals to real-world examples. You will see examples of product recommendations, ensemble learning, anomaly detection, image classification, sentiment analysis, and others. Programming examples are written with modern C++ libraries such as PyTorch C++ API,…


Machine Learning with C++ - Mask R-CNN with PyTorch C++ Frontend

Posted on January 21, 2019 at 7:44am 0 Comments

I made C++ implementation of Mask R-CNN with PyTorch C++ frontend. The code is based on PyTorch implementations from multimodallearning and Keras implementation from …


Machine Learning with C++ - Faster R-CNN with MXNet C++ Frontend

Posted on November 5, 2018 at 11:30pm 0 Comments

I published implementation of Faster R-CNN with MXNet C++ Frontend. You can use this implementation as comprehensive example of using MXNet C++ Frontend, it has custom data loader for MS Coco dataset, implements custom target proposal layer as a part of the project without modification MXNet library, contains code for errors checking (Missed in current C++ API), have Eigen and NDArray integration samples. Feel free to leave comments and proposes. The code is available on Github,…


Machine Learning with C++ - Classification with Dlib

Posted on August 7, 2018 at 7:03am 0 Comments

Dlib is an open source C++ framework containing various machine learning algorithms and many other complementary stuff which can be used for image processing, computer vision, linear algebra calculations and many other things. It has very good documentation and a lot of useful examples. In this post I will show how to use this library for solving a classification problem on Iris data…


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